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fix low-precision audio classification pipeline (#35435)

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* fix low-precision audio classification pipeline

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* add test

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix format

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix torch import

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix torch import

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix format

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

---------

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
This commit is contained in:
jiqing-feng 2025-01-21 00:20:51 +08:00 committed by GitHub
parent 641238eb76
commit f19135afc7
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2 changed files with 38 additions and 1 deletions
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2
src/transformers/pipelines/audio_classification.py
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@ -212,6 +212,8 @@ class AudioClassificationPipeline(Pipeline):
processed = self.feature_extractor(
inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
)
if self.torch_dtype is not None:
processed = processed.to(dtype=self.torch_dtype)
return processed
def _forward(self, model_inputs):

37
tests/pipelines/test_pipelines_audio_classification.py
View File

@ -17,7 +17,11 @@ import unittest
import numpy as np
from huggingface_hub import AudioClassificationOutputElement
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
is_torch_available,
)
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
compare_pipeline_output_to_hub_spec,
@ -32,6 +36,10 @@ from transformers.testing_utils import (
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class AudioClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
@ -127,6 +135,33 @@ class AudioClassificationPipelineTests(unittest.TestCase):
output = audio_classifier(audio_dict, top_k=4)
self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
@require_torch
def test_small_model_pt_fp16(self):
model = "anton-l/wav2vec2-random-tiny-classifier"
audio_classifier = pipeline("audio-classification", model=model, torch_dtype=torch.float16)
audio = np.ones((8000,))
output = audio_classifier(audio, top_k=4)
EXPECTED_OUTPUT = [
{"score": 0.0839, "label": "no"},
{"score": 0.0837, "label": "go"},
{"score": 0.0836, "label": "yes"},
{"score": 0.0835, "label": "right"},
]
EXPECTED_OUTPUT_PT_2 = [
{"score": 0.0845, "label": "stop"},
{"score": 0.0844, "label": "on"},
{"score": 0.0841, "label": "right"},
{"score": 0.0834, "label": "left"},
]
self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
audio_dict = {"array": np.ones((8000,)), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
output = audio_classifier(audio_dict, top_k=4)
self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
@require_torch
@slow
def test_large_model_pt(self):